Submitted by admin on Fri, 08/19/2022 - 09:09

Inability to obtain trusted data and/or lack of data source alignment

Sequence Number 5 Industry Seaports Banner Inability to obtain trusted data and/or lack of data source alignment How 5G enabled

Automated quality control employing machine learning with sensors collecting data and alerting during abnormal production for improvement purposes.

Data Flows
Title Devices Icon Devices Description
  • Collect all data required with or without sensors
  • Include broad spectrum of data sources to support the Digital Twin
Title Connectivity Icon Connectivity Description
  • Time series data transport
  • Camera images
  • Asset data (maintenance records) access
Title Edge Compute Icon Edge Compute Description
  • Time critical activities
  • Camera – MV interpretation
Title Cloud Compute & Storage Icon Cloud Compute & Storage Description
  • All data collected from assets (both historical and real-time)
  • Enterprise-owned storage
Title Applications & Services Icon Applications & Services Description
  • Non-time critical activities
  • Data Integration and Digital Twin type of support
  • E2E automated
Title Inform Decision Makers Icon Inform Decision Makers Description
  • Decisions made increasingly by AI processes based on AI models and decisions based on utilising Digital Twin 
Title Support Decision Making Icon Support Decision Making Description
  • End of process
Application Logic
Description
  • Identify critical assets.
  • Collect event and/or timeseries data from these critical assets, potentially using sensors.
  • Collect camera images (fixed/drones /robotics/etc.).
  • Collect any other data sources that are relevant to Digital Twin and AI models, including operational and engineering data.
  • Edge + 5G to be used for all time critical events.
Description
  • All collected data from edge sensors will be stored long-term in Enterprise storage.
  • Development of the ML model is done through an iterative process. A quality ML model (fully data-driven) will require multiple steps to detect anomalies/potential failures.
  • Models will be stored and maintained by AI applications.
  • SME involvement working with data scientist is required to develop the model.
Description
  • Data is accessible via Digital Twin: Integration and Visualisation layer.
  • Digital Twin is accessible to all Port workers.
  • The process from data collection to execution of models is fully automated.
  • Only alerts operations when anomalies are detected in data behaviour. Over time as ML model strengthens, AI decisions will increase without the need for operator involvement
  • Using XR to view 3D models
Expected benefits Key value created